Blame for phase4_tools.py

"""
Phase 4 — Enhanced Finance Tools with Real FinBERT Sentiment

Key addition over Phase 3:
  generate_investment_alert now uses ProsusAI/finbert to score the retrieved
  financial text before deciding the investment signal.  The placeholder
  sentiment from Phase 3 is replaced with actual model output.

Usage:
  from phase4_tools import build_finbert, run_finbert, ENHANCED_TOOL_REGISTRY
  # patch into Phase 3 graph at runtime (see main() below)
"""

import json
import datetime
import os

import torch
from dotenv import load_dotenv
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from phase1_ingestion import (
    PDF_PATH, CHROMA_DIR, TABLE_STORE_PATH,
    build_embeddings, build_llm, load_pdf, tag_financial_entities,
)
from phase2_retrieval import (
    advanced_retrieval_pipeline,
    build_bm25_retriever,
    build_cross_encoder,
    build_dense_retriever,
    load_or_build_vectorstore,
)
from phase3_agent import (
    build_components,
    build_graph,
    context_from_dicts,
    docs_to_dicts,
    make_initial_state,
    parse_json_from_response,
    print_result,
    # Phase 3 tools we keep unchanged
    extract_financial_metrics,
    generate_risk_summary,
    flag_compliance_issue,
    schedule_analyst_review,
)

load_dotenv()

FINBERT_MODEL = "ProsusAI/finbert"
FINBERT_LABELS = ["positive", "negative", "neutral"]
FINBERT_MAX_TOKENS = 512


# ── FinBERT loader ────────────────────────────────────────────────────────────
def build_finbert() -> tuple:
    """Load FinBERT tokenizer + model.  Returns (tokenizer, model)."""
    print(f"[FINBERT] Loading {FINBERT_MODEL}...")
    tokenizer = AutoTokenizer.from_pretrained(FINBERT_MODEL)
    model = AutoModelForSequenceClassification.from_pretrained(FINBERT_MODEL)
    model.eval()
    print("[FINBERT] Model ready")
    return tokenizer, model


# ── FinBERT inference ─────────────────────────────────────────────────────────
def run_finbert(text: str, tokenizer, model) -> dict:
    """
    Run FinBERT on a single text string (truncated to 512 tokens).
    Returns {"label": "positive|negative|neutral", "score": float,
             "all_scores": {"positive": float, "negative": float, "neutral": float}}
    """
    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        max_length=FINBERT_MAX_TOKENS,
        padding=True,
    )
    with torch.no_grad():
        logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=1)[0].tolist()
    all_scores = {FINBERT_LABELS[i]: round(probs[i], 4) for i in range(3)}
    dominant = max(all_scores, key=all_scores.get)
    return {"label": dominant, "score": all_scores[dominant], "all_scores": all_scores}


def run_finbert_on_docs(doc_dicts: list[dict], tokenizer, model) -> dict:
    """
    Run FinBERT across all retrieved doc chunks and aggregate scores.
    Strategy: average scores across all chunks, then pick the dominant label.
    This handles the common case where a single page has mixed sentiment.
    """
    if not doc_dicts:
        return {"label": "neutral", "score": 0.5,
                "all_scores": {"positive": 0.33, "negative": 0.33, "neutral": 0.34}}

    agg = {"positive": 0.0, "negative": 0.0, "neutral": 0.0}
    n = 0
    for doc in doc_dicts:
        text = doc["page_content"].strip()
        if not text:
            continue
        result = run_finbert(text, tokenizer, model)
        for label, score in result["all_scores"].items():
            agg[label] += score
        n += 1

    if n == 0:
        return {"label": "neutral", "score": 0.5,
                "all_scores": {"positive": 0.33, "negative": 0.33, "neutral": 0.34}}

    avg = {k: round(v / n, 4) for k, v in agg.items()}
    dominant = max(avg, key=avg.get)
    return {"label": dominant, "score": avg[dominant], "all_scores": avg}


# ── Enhanced generate_investment_alert (real FinBERT) ─────────────────────────
def make_enhanced_investment_alert(finbert_tokenizer, finbert_model):
    """
    Returns an enhanced generate_investment_alert function that runs real
    FinBERT sentiment before calling the LLM to decide the signal.
    Injected into ENHANCED_TOOL_REGISTRY and into the Phase 3 graph.
    """
    def generate_investment_alert(tool_input: dict, llm) -> dict:
        company  = tool_input.get("company", "Infosys")
        context  = tool_input.get("context", "")
        doc_dicts= tool_input.get("doc_dicts", [])   # injected by alert_agent

        # ── Step 1: Run real FinBERT ──────────────────────────────────────────
        # Prefer doc_dicts for per-chunk scoring; fall back to raw context string
        if doc_dicts:
            finbert_result = run_finbert_on_docs(doc_dicts, finbert_tokenizer, finbert_model)
        else:
            finbert_result = run_finbert(context[:FINBERT_MAX_TOKENS],
                                         finbert_tokenizer, finbert_model)

        print(f"[FINBERT] sentiment={finbert_result['label']} "
              f"({finbert_result['score']:.2f}) | "
              f"all={finbert_result['all_scores']}")

        # ── Step 2: Let LLM decide signal using FinBERT as a hard input ──────
        sentiment_hint = (
            f"FinBERT (domain-specific sentiment model) scored this text as: "
            f"{finbert_result['label'].upper()} "
            f"(confidence {finbert_result['score']:.2f}). "
            f"All scores — positive: {finbert_result['all_scores']['positive']}, "
            f"negative: {finbert_result['all_scores']['negative']}, "
            f"neutral: {finbert_result['all_scores']['neutral']}."
        )

        prompt = f"""You are a senior investment analyst.
{sentiment_hint}

Using BOTH the FinBERT sentiment above AND the financial context below,
generate a structured investment alert for {company}.

Return ONLY valid JSON:
{{
  "company": "{company}",
  "signal": "buy|sell|hold|watch",
  "trigger_reason": "...",
  "confidence_score": <0.0-1.0>,
  "supporting_evidence": ["exact quote 1", "exact quote 2"],
  "finbert_sentiment": {{
    "label": "{finbert_result['label']}",
    "score": {finbert_result['score']},
    "all_scores": {{
      "positive": {finbert_result['all_scores']['positive']},
      "negative": {finbert_result['all_scores']['negative']},
      "neutral":  {finbert_result['all_scores']['neutral']}
    }}
  }}
}}

Financial context:
{context[:3000]}"""

        try:
            return parse_json_from_response(llm.invoke(prompt).content)
        except Exception as e:
            # Fallback: return what we have with FinBERT scores populated
            return {
                "company": company,
                "signal": "hold",
                "trigger_reason": "LLM parse error — FinBERT scores still valid",
                "confidence_score": finbert_result["score"],
                "supporting_evidence": [],
                "finbert_sentiment": finbert_result,
                "error": str(e),
            }

    return generate_investment_alert


# ── Build patched components dict ─────────────────────────────────────────────
def build_phase4_components() -> dict:
    """
    Builds all Phase 3 components then loads FinBERT and patches
    generate_investment_alert with the real-FinBERT version.
    """
    components = build_components()                   # Phase 3 setup

    finbert_tokenizer, finbert_model = build_finbert()
    enhanced_fn = make_enhanced_investment_alert(finbert_tokenizer, finbert_model)

    # Patch the tool registry in the components dict so the graph uses it
    components["finbert_tokenizer"] = finbert_tokenizer
    components["finbert_model"]     = finbert_model
    components["enhanced_alert_fn"] = enhanced_fn

    return components


# ── Patch alert agent to pass doc_dicts to the tool ──────────────────────────
def build_phase4_graph(components: dict):
    """
    Builds the Phase 3 graph but overrides the alert_agent_node closure
    so it passes doc_dicts to generate_investment_alert for per-chunk FinBERT.
    """
    from phase3_agent import (
        build_agent_nodes, route_by_intent, route_self_rag,
        INTENT_TO_TOOL, AgentState,
    )
    from langgraph.graph import StateGraph, END

    # Build standard Phase 3 nodes
    nodes = build_agent_nodes(components)

    llm             = components["llm"]
    bm25_retriever  = components["bm25_retriever"]
    dense_retriever = components["dense_retriever"]
    cross_encoder   = components["cross_encoder"]
    enhanced_alert  = components["enhanced_alert_fn"]

    # ── Override alert_agent_node ─────────────────────────────────────────────
    def alert_agent_node_v4(state: AgentState) -> dict:
        print(f"[ALERT AGENT v4] Analysing: {state['query'][:60]}")
        retrieval_query = (
            f"revenue profit growth performance outlook risk sentiment {state['query']}"
        )
        docs = advanced_retrieval_pipeline(
            retrieval_query, bm25_retriever, dense_retriever, cross_encoder, llm
        )
        doc_dicts = docs_to_dicts(docs)
        citations = list({d["metadata"].get("page") for d in doc_dicts})
        context   = context_from_dicts(doc_dicts)

        answer_prompt = (
            "You are an investment analyst. Assess the company's financial health "
            "and generate a clear investment signal with supporting evidence. "
            "Real FinBERT sentiment has already been computed and will be included.\n\n"
            f"Retrieved context:\n{context[:4000]}\n\n"
            f"Question: {state['query']}\n\nAnswer with specific facts and page citations:"
        )
        answer = llm.invoke(answer_prompt).content

        # Pass doc_dicts so enhanced_alert can run FinBERT per chunk
        tool_input = {
            "company":   "Infosys",
            "context":   context,
            "doc_dicts": doc_dicts,
        }
        print(f"[ALERT AGENT v4] {len(doc_dicts)} docs | citations={citations}")
        return {
            "retrieved_docs": doc_dicts,
            "final_answer":   answer,
            "citations":      citations,
            "tool_name":      "generate_investment_alert",
            "tool_input":     tool_input,
        }

    nodes["alert_agent"] = alert_agent_node_v4

    # ── Override tool_executor to use enhanced tool for investment_alert ──────
    def tool_executor_node_v4(state: AgentState) -> dict:
        tool_name = state.get("tool_name")
        from phase3_agent import TOOL_REGISTRY

        # Use enhanced alert function, standard registry for everything else
        registry = {**TOOL_REGISTRY, "generate_investment_alert": enhanced_alert}

        if not tool_name or tool_name not in registry:
            print("[TOOL EXECUTOR v4] No tool — passing answer through")
            return {"tool_output": {"answer": state["final_answer"]}}

        print(f"[TOOL EXECUTOR v4] Running: {tool_name}")
        output = registry[tool_name](state["tool_input"], llm)
        print(f"[TOOL EXECUTOR v4] Done — keys: {list(output.keys())}")
        return {"tool_output": output}

    nodes["tool_executor"] = tool_executor_node_v4

    # ── Compile graph ─────────────────────────────────────────────────────────
    workflow = StateGraph(AgentState)
    for name, fn in nodes.items():
        workflow.add_node(name, fn)
    workflow.set_entry_point("orchestrator")
    workflow.add_conditional_edges(
        "orchestrator", route_by_intent,
        {
            "rag_agent":          "rag_agent",
            "compliance_agent":   "compliance_agent",
            "alert_agent":        "alert_agent",
            "finance_calculator": "finance_calculator",
        },
    )
    for specialist in ["rag_agent", "compliance_agent",
                       "alert_agent", "finance_calculator"]:
        workflow.add_edge(specialist, "tool_executor")
    workflow.add_edge("tool_executor", "self_rag_evaluator")
    workflow.add_conditional_edges(
        "self_rag_evaluator", route_self_rag,
        {"rag_agent": "rag_agent", "end": END},
    )
    return workflow.compile()


# ── Standalone FinBERT demo ───────────────────────────────────────────────────
def run_finbert_demo(finbert_tokenizer, finbert_model) -> None:
    """Show FinBERT scoring on representative Infosys sentences."""
    print("\n" + "=" * 65)
    print("FINBERT STANDALONE DEMO")
    print("=" * 65)
    sentences = [
        "Revenues grew 6.1% year-on-year to ₹1,62,990 crore in FY25.",
        "Revenue fell short of analyst estimates amid macro uncertainty.",
        "Operating margin improved to 21.1%, up from 20.7% in FY24.",
        "The company faces risks from geopolitical tensions and talent attrition.",
        "Infosys won large AI-led transformation deals across key verticals.",
        "Net headcount declined as the company focused on utilisation.",
    ]
    for sentence in sentences:
        result = run_finbert(sentence, finbert_tokenizer, finbert_model)
        bar = "+" * int(result["all_scores"]["positive"] * 20)
        print(f"  [{result['label']:8s} {result['score']:.2f}] {sentence[:55]}")
        print(f"           pos={result['all_scores']['positive']:.3f}  "
              f"neg={result['all_scores']['negative']:.3f}  "
              f"neu={result['all_scores']['neutral']:.3f}")


# ── Main ──────────────────────────────────────────────────────────────────────
if __name__ == "__main__":
    # Build all components (Phase 3 + FinBERT)
    components = build_phase4_components()

    # Show FinBERT standalone demo
    run_finbert_demo(components["finbert_tokenizer"], components["finbert_model"])

    # Build Phase 4 graph (Phase 3 graph + patched alert agent + enhanced tool)
    app = build_phase4_graph(components)
    print("\n=== Phase 4 LangGraph compiled successfully ===\n")

    # Test: investment alert query (the one that exercises FinBERT)
    test_queries = [
        "Generate an investment alert for Infosys based on their FY25 performance.",
        "Should I be concerned about Infosys given the risk factors in their latest filing?",
    ]

    for query in test_queries:
        print(f"\n{'#' * 65}")
        result = app.invoke(make_initial_state(query))
        print_result(result)